Depth-resolved information is essential for ocean research. For this study, we developed a shipborne photon-counting lidar for depth-resolved oceanic plankton observation. A pulsed fiber laser with frequency doubling to 532 nm acts as a light source, generating a single pulse at the micro-joule level with a pulse width of less than 1 ns. The receiver is capable of simultaneously detecting the elastic signal at two orthogonal polarization states, the Raman scattering from seawater, and the fluorescence signal from chlorophyll A. The data acquisition system utilizes the photon-counting technique to record each photon event, after which the backscattering signal intensity can be recovered by counting photons from multiple pulses. Benefitting from the immunity of this statistical detection method to the ringing effect of the detector and amplifier circuit, high-sensitivity and high-linearity backscatter signal measurements are realized. In this paper, we analyze and correct the after-pulse phenomenon of high-linearity signals through experiments and theoretical simulations. Through the after-pulse correction, the lidar attenuation coefficient retrieved from the corrected signal are in good agreement with the diffuse attenuation coefficients calculated from the in situ instrument, indicating the potential of this shipborne photon-counting lidar for ocean observation applications.

This paper reports a complete solution for short-acquisition-time, flood-illumination, and non-scanning 3D imaging. Based on an InGaAs/InP 64×64 Geiger-mode avalanche photodiode array, we developed a single-photon imaging Lidar with a field of view of 19.2 mrad. A spatiotemporal dominant imaging method was also applied for depth estimation under high daytime background noise conditions. In the field tests, 3D imaging information was effectively extracted from objects with strong solar background light. An acquisition time of only 0.5 ms to 4 ms was needed for a building target approximately 1300 m away. The experimental results show that the Lidar and its data processing method have the potential for dynamic scenarios that require high performance 3D imaging, such as vehicle navigation, airborne mapping, and moving target monitoring.

Geiger-mode single-photon LiDAR is an important tool for long-distance three-dimensional remote sensing. A planar-array-based photon counting LiDAR that uses 32-by-32 fiber arrays coupled to an optical lens as a transceiver unit was developed. Using transmitters and receivers with the same design, the proposed device easily achieves a high-precision alignment of 1024 pixels and flexible detection field-of-view design. The LiDAR uses a set of relay lenses to couple echoes from the receiving fiber arrays to the pixels of a planar-array single-photon detector, which has a resolution enhanced by a factor of four (64-by-64) relative to the fiber array to reduce cross talk from neighboring pixels. The results of field experiments demonstrate that the proposed LiDAR can reconstruct a three-dimensional image from a distance of 1600 m. Even at an acquisition time of only 40 ms, targets with an area of approximately 50% can still be identified from 200 frames. These results demonstrate the potential of the LiDAR prototype for use in instantaneous high-density point-array measurement and long-range wide-FoV 3D imaging, which can be used in remote sensing applications such as airborne surveys and mapping. In the future, we will integrate the proposed LiDAR prototype and the pose measurement system to take the aircraft-based 3D imaging remote sensing experiments.

Photon-counting LiDAR can obtain long-distance, high-precision target3D geographic information, but extracting high-precision signal photons from background noise photons is the key premise of photon-counting LiDAR data processing and application. This study proposes an adaptive noise filtering algorithm that adjusts parameters according to the background photon count rate and removes noise photons based on the local mean Euclidean distance. A simulated photon library that provides different background photon count rates and detection probabilities was constructed. It was then used to fit the distribution relationship between the background photon count rate and the average KNN (K-Nearest Neighbor) distance ($k$ = 2–6) and to obtain the optimal denoising threshold under different background photon count rates. Finally, the proposed method was evaluated by comparing it with the modified density-based spatial clustering (mDBSCAN) and local distance-based statistical methods. The experimental results show that various methods are similar when the background noise rate is high. However, at most non-extreme background photon count rate levels, the F of this algorithm was maintained between 0.97–0.99, which is an improvement over other classical algorithms. The new strategy eliminated the artificial introduction of errors. Due to its low error rates, the proposed method can be widely applied in photon-counting LiDAR signal extraction under various conditions.

Unmanned Aerial Vehicle (UAV) based photon-counting ocean bathymetric light detection and ranging (LiDAR) systems rapidly acquire topographic data from islands, reefs, and shallow waters. However, the bathymetric ability of seabed topography is affected by high backscattering from the sea surface owing to its proximity, and no suitable imaging models are available. Herein, we designed a novel ray approach for bathymetry based on a dual-polarization photon-counting LiDAR. Based on the transmission characteristics of light, a dual-polarization channel strategy was proposed, and the data from two channels in vegetation, sand, and shallow and medium-depth waters were compared. Based on the ray tracing method, imaging models of the water surface and depth for light and small photon-counting LiDAR were established. Shallow-water experiments were conducted near Jiajing Island, Hainan, China, verified the accuracy of the LiDAR bathymetry data by shipborne single-beam sounding data. The results indicate that the vertical polarization channel data had a high signal-to-noise ratio (SNR) in the terrestrial part, while the horizontal polarization channel had better water surface backscatter suppression effect and strong bathymetry ability in the water part. The detectable depth was approximately 8 m in the experimental area. The MAEs of the depth values of the LiDAR point cloud before and after refraction correction relative to the single beam depth measurement values were 1.08 and 0.09m, respectively. And the RMSEs before and after correction were 1.12 and 0.11 m, respectively.

We report an eye-safe aerosol and cloud lidar with an Erbium-doped fiber laser (EDFL) and a free-space intracavity upconversion detector as the transmitter and receiver, respectively. The EDFL was home-made, which could produce linearly-polarized pulses at a repetition rate of 15 kHz with pulse energies of ~70 μJ and pulse durations of ~7 ns centered at 1550 nm. The echo photons were upconverted to ~631 nm via the sum frequency generation process in a bow-tie cavity, where a Nd:YVO₄ and a PPLN crystal served as the pump and nonlinear frequency conversion devices, respectively. The upconverted visible photons were recorded by a photomultiplier tube and their timestamps were registered by a customized time-to-digital converter for distance-resolved measurement. Reflected signals peaked at ~6.8 km from a hard target were measured with a distance resolution of 0.6 m for an integral duration of 10 s. Atmospheric backscattered signals, with a range of ~6 km, were also detectable for longer integral durations. The evolution of aerosols and clouds were recorded by this lidar in a preliminary experiment with a continuous measuring time of over 18 h. Clear boundary and fine structures of clouds were identified with a spatial resolution of 9.6 m during the measurement, showing its great potential for practical aerosol and cloud monitoring.